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"""
Train SAR-DDPM model.
"""
import argparse
import torch.nn.functional as F
from guided_diffusion import dist_util, logger
from guided_diffusion.image_datasets import load_data
from guided_diffusion.resample import create_named_schedule_sampler
from guided_diffusion.script_util import (
sr_model_and_diffusion_defaults,
sr_create_model_and_diffusion,
args_to_dict,
add_dict_to_argparser,
)
from guided_diffusion.train_util import TrainLoop
from torch.utils.data import DataLoader
# from train_dataset import TrainData
from valdata import ValData, ValDataNew
train_dir = 'path_to_training_data/'
val_dir = 'path_to_validation_data/'
pretrained_weight_path = "./weights/64_256_upsampler.pt"
def main():
args = create_argparser().parse_args()
dist_util.setup_dist()
logger.configure()
logger.log("creating model...")
model, diffusion = sr_create_model_and_diffusion(
**args_to_dict(args, sr_model_and_diffusion_defaults().keys())
)
model.to(dist_util.dev())
schedule_sampler = create_named_schedule_sampler(args.schedule_sampler, diffusion)
logger.log("creating data loader...")
val_data = DataLoader(ValDataNew(dataset_path=val_dir), batch_size=1, shuffle=False, num_workers=1)
print(args)
data = load_sar_data(
args.data_dir,
train_dir,
args.batch_size,
large_size=256,
small_size=256,
class_cond=False,
)
logger.log("training...")
TrainLoop(
model=model,
diffusion=diffusion,
data=data,
val_dat=val_data,
batch_size=args.batch_size,
microbatch=args.microbatch,
lr=args.lr,
ema_rate=args.ema_rate,
log_interval=args.log_interval,
save_interval=args.save_interval,
resume_checkpoint=args.resume_checkpoint,
args = args,
use_fp16=args.use_fp16,
fp16_scale_growth=args.fp16_scale_growth,
schedule_sampler=schedule_sampler,
weight_decay=args.weight_decay,
lr_anneal_steps=args.lr_anneal_steps,
).run_loop()
def load_sar_data(data_dir,gt_dirs, batch_size, large_size, small_size, class_cond=False):
data = load_data(
data_dir=data_dir,
gt_dir=gt_dirs,
batch_size=batch_size,
image_size=large_size,
class_cond=False,
)
for large_batch, model_kwargs in data:
yield large_batch, model_kwargs
def create_argparser():
defaults = dict(
data_dir = train_dir,
schedule_sampler="uniform",
lr=1e-4,
# lr=5e-5,
weight_decay=0.0,
lr_anneal_steps=0,
batch_size=2,
microbatch=1,
ema_rate="0.9999",
log_interval=1000,
save_interval=10,
resume_checkpoint=pretrained_weight_path,
use_fp16=False,
fp16_scale_growth=1e-3,
)
defaults.update(sr_model_and_diffusion_defaults())
parser = argparse.ArgumentParser()
add_dict_to_argparser(parser, defaults)
return parser
if __name__ == "__main__":
main()
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